OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of Human Awareness of Security and Privacy Threats in Smart Environments
Venue: arXiv cs.CY 2015
Year: 2015

Smart environments integrate Information and Communication Technologies (ICT) into devices, vehicles, buildings and cities to offer an increased quality of life, energy efficiency and economical sustainability. In this perspective, the individual has a core role and so has networking, which enables such entities to cooperate. However, the huge amount of sensitive data, social aspects and the mixed set of protocols offer many opportunities to inject hazards, exfiltrate information, mass profiling of citizens, or produce a new wave of attacks. This work reviews the major risks arising from the usage of ICT-techniques for smart environments, with emphasis on networking. Its main contribution is to explain the role of different stakeholders for causing a lack of security and to envision future threats by considering human aspects.
Compactly Supported Wavelets Derived From Legendre Polynomials: Spherical Harmonic Wavelets
Venue: arXiv cs.NA 2015
Year: 2015

A new family of wavelets is introduced, which is associated with Legendre polynomials. These wavelets, termed spherical harmonic or Legendre wavelets, possess compact support. The method for the wavelet construction is derived from the association of ordinary second order differential equations with multiresolution filters. The low-pass filter associated with Legendre multiresolution analysis is a linear phase finite impulse response filter (FIR).
The Sample Complexity of Revenue Maximization
Venue: arXiv cs.GT 2015
Year: 2015

In the design and analysis of revenue-maximizing auctions, auction performance is typically measured with respect to a prior distribution over inputs. The most obvious source for such a distribution is past data. The goal is to understand how much data is necessary and sufficient to guarantee near-optimal expected revenue. Our basic model is a single-item auction in which bidders' valuations are drawn independently from unknown and non-identical distributions. The seller is given $m$ samples from each of these distributions "for free" and chooses an auction to run on a fresh sample. How large does m need to be, as a function of the number k of bidders and eps > 0, so that a (1 - eps)-approximation of the optimal revenue is achievable? We prove that, under standard tail conditions on the underlying distributions, m = poly(k, 1/eps) samples are necessary and sufficient. Our lower bound stands in contrast to many recent results on simple and prior-independent auctions and fundamentally involves the interplay between bidder competition, non-identical distributions, and a very close (but still constant) approximation of the optimal revenue. It effectively shows that the only way to achieve a sufficiently good constant approximation of the optimal revenue is through a detailed understanding of bidders' valuation distributions. Our upper bound is constructive and applies in particular to a variant of the empirical Myerson auction, the natural auction that runs the revenue-maximizing auction with respect to the empirical distributions of the samples. Our sample complexity lower bound depends on the set of allowable distributions, and to capture this we introduce alpha-strongly regular distributions, which interpolate between the well-studied classes of regular (alpha = 0) and MHR (alpha = 1) distributions. We give evidence that this definition is of independent interest.
A Novel architecture for improving performance under virtualized environments
Venue: arXiv cs.DC 2015
Year: 2015

Even though virtualization provides a lot of advantages in cloud computing, it does not provide effective performance isolation between the virtualization machines. In other words, the performance may get affected due the interferences caused by co-virtual machines. This can be achieved by the proper management of resource allocations between the Virtual Machines running simultaneously. This paper aims at providing a proposed novel architecture that is based on Fast Genetic K-means++ algorithm and test results show positive improvements in terms of performance improvements over a similar existing approach.
What Can Wireless Cellular Technologies Do about the Upcoming Smart Metering Traffic?
Venue: arXiv cs.IT 2015
Year: 2015

The introduction of smart electricity meters with cellular radio interface puts an additional load on the wireless cellular networks. Currently, these meters are designed for low duty cycle billing and occasional system check, which generates a low-rate sporadic traffic. As the number of distributed energy resources increases, the household power will become more variable and thus unpredictable from the viewpoint of the Distribution System Operator (DSO). It is therefore expected, in the near future, to have an increased number of Wide Area Measurement System (WAMS) devices with Phasor Measurement Unit (PMU)-like capabilities in the distribution grid, thus allowing the utilities to monitor the low voltage grid quality while providing information required for tighter grid control. From a communication standpoint, the traffic profile will change drastically towards higher data volumes and higher rates per device. In this paper, we characterize the current traffic generated by smart electricity meters and supplement it with the potential traffic requirements brought by introducing enhanced Smart Meters, i.e., meters with PMU-like capabilities. Our study shows how GSM/GPRS and LTE cellular system performance behaves with the current and next generation smart meters traffic, where it is clearly seen that the PMU data will seriously challenge these wireless systems. We conclude by highlighting the possible solutions for upgrading the cellular standards, in order to cope with the upcoming smart metering traffic.
A Multiple-Expert Binarization Framework for Multispectral Images
Venue: arXiv cs.CV 2015
Year: 2015

In this work, a multiple-expert binarization framework for multispectral images is proposed. The framework is based on a constrained subspace selection limited to the spectral bands combined with state-of-the-art gray-level binarization methods. The framework uses a binarization wrapper to enhance the performance of the gray-level binarization. Nonlinear preprocessing of the individual spectral bands is used to enhance the textual information. An evolutionary optimizer is considered to obtain the optimal and some suboptimal 3-band subspaces from which an ensemble of experts is then formed. The framework is applied to a ground truth multispectral dataset with promising results. In addition, a generalization to the cross-validation approach is developed that not only evaluates generalizability of the framework, it also provides a practical instance of the selected experts that could be then applied to unseen inputs despite the small size of the given ground truth dataset.
Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets
Venue: arXiv cs.CV 2015
Year: 2015

Sliding window is one direct way to extend a successful recognition system to handle the more challenging detection problem. While action recognition decides only whether or not an action is present in a pre-segmented video sequence, action detection identifies the time interval where the action occurred in an unsegmented video stream. Sliding window approaches for action detection can however be slow as they maximize a classifier score over all possible sub-intervals. Even though new schemes utilize dynamic programming to speed up the search for the optimal sub-interval, they require offline processing on the whole video sequence. In this paper, we propose a novel approach for online action detection based on 3D skeleton sequences extracted from depth data. It identifies the sub-interval with the maximum classifier score in linear time. Furthermore, it is invariant to temporal scale variations and is suitable for real-time applications with low latency.
A proposal for regularly updated review/survey articles: "Perpetual Reviews"
Venue: arXiv cs.DL 2015
Year: 2015

We advocate the publication of review/survey articles that will be updated regularly, both in traditional journals and novel venues. We call these "perpetual reviews." This idea naturally builds on the dissemination and archival capabilities present in the modern internet, and indeed perpetual reviews exist already in some forms. Perpetual review articles allow authors to maintain over time the relevance of non-research scholarship that requires a significant investment of effort. Further, such reviews published in a purely electronic format without space constraints can also permit more pedagogical scholarship and clearer treatment of technical issues that remain obscure in a brief treatment.
The Arithmetic Cosine Transform: Exact and Approximate Algorithms
Venue: arXiv cs.NA 2015
Year: 2015

In this paper, we introduce a new class of transform method --- the arithmetic cosine transform (ACT). We provide the central mathematical properties of the ACT, necessary in designing efficient and accurate implementations of the new transform method. The key mathematical tools used in the paper come from analytic number theory, in particular the properties of the Riemann zeta function. Additionally, we demonstrate that an exact signal interpolation is achievable for any block-length. Approximate calculations were also considered. The numerical examples provided show the potential of the ACT for various digital signal processing applications.
Phantom cascades: The effect of hidden nodes on information diffusion
Venue: arXiv cs.SI 2015
Year: 2015

Research on information diffusion generally assumes complete knowledge of the underlying network. However, in the presence of factors such as increasing privacy awareness, restrictions on application programming interfaces (APIs) and sampling strategies, this assumption rarely holds in the real world which in turn leads to an underestimation of the size of information cascades. In this work we study the effect of hidden network structure on information diffusion processes. We characterise information cascades through activation paths traversing visible and hidden parts of the network. We quantify diffusion estimation error while varying the amount of hidden structure in five empirical and synthetic network datasets and demonstrate the effect of topological properties on this error. Finally, we suggest practical recommendations for practitioners and propose a model to predict the cascade size with minimal information regarding the underlying network.
A Framework for Symmetric Part Detection in Cluttered Scenes
Venue: arXiv cs.CV 2015
Year: 2015

The role of symmetry in computer vision has waxed and waned in importance during the evolution of the field from its earliest days. At first figuring prominently in support of bottom-up indexing, it fell out of favor as shape gave way to appearance and recognition gave way to detection. With a strong prior in the form of a target object, the role of the weaker priors offered by perceptual grouping was greatly diminished. However, as the field returns to the problem of recognition from a large database, the bottom-up recovery of the parts that make up the objects in a cluttered scene is critical for their recognition. The medial axis community has long exploited the ubiquitous regularity of symmetry as a basis for the decomposition of a closed contour into medial parts. However, today's recognition systems are faced with cluttered scenes, and the assumption that a closed contour exists, i.e. that figure-ground segmentation has been solved, renders much of the medial axis community's work inapplicable. In this article, we review a computational framework, previously reported in Lee et al. (2013), Levinshtein et al. (2009, 2013), that bridges the representation power of the medial axis and the need to recover and group an object's parts in a cluttered scene. Our framework is rooted in the idea that a maximally inscribed disc, the building block of a medial axis, can be modeled as a compact superpixel in the image. We evaluate the method on images of cluttered scenes.
The Number of Holes in the Union of Translates of a Convex Set in Three Dimensions
Venue: arXiv cs.CG 2015
Year: 2015

We show that the union of $n$ translates of a convex body in $\mathbb{R}^3$ can have $Θ(n^3)$ holes in the worst case, where a hole in a set $X$ is a connected component of $\mathbb{R}^3 \setminus X$. This refutes a 20-year-old conjecture. As a consequence, we also obtain improved lower bounds on the complexity of motion planning problems and of Voronoi diagrams with convex distance functions.
Linearized Reed-Solomon codes and linearized Wenger graphs
Venue: arXiv cs.IT 2015
Year: 2015

A codeword is associated to a linearized polynomial. The weight distribution of the codewords is determined as the linearized polynomial varies in a family of fixed degree. There is a corresponding result on Wenger graphs from linearized polynomials.
Low-Complexity Widely-Linear Precoding for Downlink Large-Scale MU-MISO Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this letter, we present a widely-linear minimum mean square error (WL-MMSE) precoding scheme employing real-valued transmit symbols for downlink large-scale multi-user multiple-input single-output (MU-MISO) systems. In contrast to the existing WL-MMSE transceivers for single-user multiple-input multiple-output (SU-MIMO) systems, which use both WL precoders and WL detectors, the proposed scheme uses WL precoding only and simple conventional detection at the user terminals (UTs). Moreover, to avoid the computational complexity associated with inversion of large matrices, we modify the WL-MMSE precoder using polynomial expansion (PE). Our simulation results show that in overloaded systems, where the number of UTs is larger than the number of base station antennas, the proposed PE WL-MMSE precoder with only a few terms in the matrix polynomial achieves a substantially higher sum rate than systems employing conventional MMSE precoding. Hence, more UTs sharing the same time/frequency resources can be served in a cell. We validate our simulation results with an analytical expression for the asymptotic sum rate which is obtained by using results from random matrix theory.
Tree Search and Quantum Computation
Venue: arXiv cs.DS 2015
Year: 2015

Traditional tree search algorithms supply a blueprint for modeling problem solving behaviour. A diverse spectrum of problems can be formulated in terms of tree search. Quantum computation, in particular Grover's algorithm, has aroused a great deal of interest since it allows for a quadratic speedup to be obtained in search procedures. In this work we consider the impact of incorporating classical search concepts alongside Grover's algorithm into a hybrid quantum search system. Some of the crucial points examined include: (1) the reverberations of contemplating the use of non-constant branching factors; (2) determining the consequences of incorporating an heuristic perspective into a quantum tree search model.
Editorial for the Proceedings of the Workshop Knowledge Maps and Information Retrieval (KMIR2014) at Digital Libraries 2014
Venue: arXiv cs.IR 2015
Year: 2015

Knowledge maps are promising tools for visualizing the structure of large-scale information spaces, but still far away from being applicable for searching. The first international workshop on "Knowledge Maps and Information Retrieval (KMIR)", held as part of the International Conference on Digital Libraries 2014 in London, aimed at bringing together experts in Information Retrieval (IR) and knowledge mapping in order to discuss the potential of interactive knowledge maps for information seeking purposes.
How can heat maps of indexing vocabularies be utilized for information seeking purposes?
Venue: arXiv cs.IR 2015
Year: 2015

The ability to browse an information space in a structured way by exploiting similarities and dissimilarities between information objects is crucial for knowledge discovery. Knowledge maps use visualizations to gain insights into the structure of large-scale information spaces, but are still far away from being applicable for searching. The paper proposes a use case for enhancing search term recommendations by heat map visualizations of co-word relation-ships taken from indexing vocabulary. By contrasting areas of different "heat" the user is enabled to indicate mainstream areas of the field in question more easily.
Wavelet based Watermarking approach in the Compressive Sensing Scenario
Venue: arXiv cs.MM 2015
Year: 2015

Due to the wide distribution and usage of digital media, an important issue is protection of the digital content. There is a number of algorithms and techniques developed for the digital watermarking.In this paper, the invisible image watermark procedure is considered. Watermark is created as a pseudo random sequence, embedded in the certain region of the image, obtained using Haar wavelet decomposition. Generally, the watermarking procedure should be robust to the various attacks-filtering, noise etc. Here we assume the Compressive sensing scenario as a new signal processing technique that may influence the robustness. The focus of this paper was the possibility of the watermark detection under Compressive Sensing attack with different number of available image coefficients. The quality of the reconstructed images has been evaluated using Peak Signal to Noise Ratio (PSNR).The theory is supported with experimental results.
Casper: Debugging Null Dereferences with Dynamic Causality Traces
Venue: arXiv cs.SE 2015
Year: 2015

Fixing a software error requires understanding its root cause. In this paper, we introduce ''causality traces'', crafted execution traces augmented with the information needed to reconstruct the causal chain from the root cause of a bug to an execution error. We propose an approach and a tool, called Casper, for dynamically constructing causality traces for null dereference errors. The core idea of Casper is to inject special values, called ''ghosts'', into the execution stream to construct the causality trace at runtime. We evaluate our contribution by providing and assessing the causality traces of 14 real null dereference bugs collected over six large, popular open-source projects. Over this data set, Casper builds a causality trace in less than 5 seconds.
Efficient Cache Availability Management in Information-Centric Networks
Venue: arXiv cs.NI 2015
Year: 2015

In-network caching is one of the fundamental operations of Information-centric networks (ICN). The default caching strategy taken by most of the current ICN proposals is caching along--default--path, which makes popular objects to be cached redundantly across the network, resulting in a low utilization of available cache space. On the other hand, efficient use of network-wide cache space requires possible cooperation among caching routers without the use of excessive signaling burden. While most of the cache optimization efforts strive to improve the latency and the overall traffic efficiency, we have taken a different path in this work and improved the storage efficiency of the cache space so that it is utilized to its most. In this work we discuss the ICN caching problem, and propose a novel distributed architecture to efficiently use the network-wide cache storage space based on distributed caching. The proposal achieves cache retention efficiency by means of controlled traffic redirection and selective caching. We utilize the ICN mechanisms and routing protocol messages for decision making, thus reducing the overall signaling need. Our proposal achieves almost 9-fold increase in cache storage efficiency, and around 20% increase in server load reduction when compared to the classic caching methods used in contemporary ICN proposals.
Entropy-power inequality for weighted entropy
Venue: arXiv cs.IT 2015
Year: 2015

We analyse an analog of the entropy-power inequality for the weighted entropy.
A comparison of the Extrapolated Successive Overrelaxation and the Preconditioned Simultaneous Displacement methods for augmented linear systems
Venue: arXiv cs.NA 2015
Year: 2015

In this paper we study the impact of two types of preconditioning on the numerical solution of large sparse augmented linear systems. The first preconditioning matrix is the lower triangular part whereas the second is the product of the lower triangular part with the upper triangular part of the augmented system's coefficient matrix. For the first preconditioning matrix we form the Generalized Modified Extrapolated Successive Overrelaxation (GMESOR) method, whereas the second preconditioning matrix yields the Generalized Modified Preconditioned Simultaneous Displacement (GMPSD) method, which is an extrapolated form of the Symmetric Successive Overrelaxation method. We find sufficient conditions for each aforementioned iterative method to converge. In addition, we develop a geometric approach, for determining the optimum values of their parameters and corresponding spectral radii. It is shown that both iterative methods studied (GMESOR and GMPSD) attain the same rate of convergence. Numerical results confirm our theoretical expectations.
Online Algorithms for a Generalized Parallel Machine Scheduling Problem
Venue: arXiv cs.DC 2015
Year: 2015

We consider different online algorithms for a generalized scheduling problem for parallel machines, described in details in the first section. This problem is the generalization of the classical parallel machine scheduling problem, when the make-span is minimized; in that case each job contains only one task. On the other hand, the problem in consideration is still a special version of the workflow scheduling problem. We present several heuristic algorithms and compare them by computer tests.
Teachable Agent
Venue: arXiv cs.CY 2015
Year: 2015

Teachable Agent (TA) is a special type of pedagogical agent which instantiates the educational theory of Learning by Teaching. Soon after its emergence, research of TA becomes an active field, as it can solve the over scaffolded problem in traditional pedagogical systems, and encourage students to take the responsibility of learning. Apart from the benefits, existing TA design also has limitations. One is the lack of enough proactive interactions with students during the learning process, and the other is the lack of believability to arouse students empathy so as to offer students an immersive learning experience. To solve these two problems, we propose a new type of TA, Affective Teachable Agent, and use a goal oriented approach to design and implement the agent system allowing agents to proactively interact with students with affective expressions. The ATA model begins with the analysis of pedagogical requirements and teaching goals, using Learning by Teaching theory to design interventions which can authentically promote the learning behaviors of students. Two crucial capabilities of ATA are highlighted Teachability, to learn new knowledge and apply the knowledge to certain tasks, and Affectivability, to establish good relationship with students and encourage them to teach well. Through executing a hierarchy of goals, the proposed TA can interact with students by pursuing its own agenda. When a student teaches the agent, the agent is performed as a naive learning companion, and when an educator teaches the agent during the design and maintenance time, the agent can perform as an authoring tool. To facilitate the involvement of educators into the game design, we develop an authoring tool for proposed ATA system, which can encapsulate the technical details and provide educational experts a natural way to convey domain knowledge to agent knowledge base.